Skew detection is relevant to a wide variety of applications, and particularly in document image processing tasks. When scanning or photocopying a paper document, for example, skew may occur in the output. Causes can include incorrect document placement on the platen glass; or movement of the object while closing the scanner lid. In scanners, photocopiers and fax machines with an Automatic Document Feed (ADF), skew can be introduced when inserting the document or due to slight misalignment by the ADF itself.
When skew occurs, the user of the document processing device would like it to be corrected. Skew correction also promotes more accurate Optical Character Recognition (OCR) operations, and the de-skewed (skew-corrected) document is more likely to be useful for subsequent downstream operations such as archiving, modifying, collaborating, communicating or printing.
In general, state-of-the-art image processing algorithms for skew detection and correction are unsuitable for on-platform, real-time and robust implementation. Known skew detection algorithms are typically based only on one source of skew information from the document image. As a result, these skew detection algorithms work well only for a certain, limited classes of documents and are not generally applicable across the whole range of documents that may be encountered in practice; which is desirable for robust embedded implementation within a device.